INVESTIGATION OF ICA ALGORITHMS FOR FEATURE

EXTRACTION OF EEG SIGNALS IN DISCRIMINATION OF

ALZHEIMER DISEASE

Jordi Sol´e-Casals

Signal Processing Group, University of Vic, Sagrada Fam´ılia 7, 08500 Vic, Spain

Franc¸ois Vialatte, Zhe Chen, Andrzej Cichocki

RIKEN Brain Science Institute, LABSP, 2-1 Hirosawa, Saitama, 351-0106 Wako-Shi, Japan

Keywords:

EEG, Alzheimer disease, ICA, BSS, Feature extraction.

Abstract:

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algo-

rithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extrac-

tion) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination

between AD (or mild cognitive impairment, MCI) and age-match control subjects.

1 INTRODUCTION

Independent component analysis (ICA) is a method

for recovering underlying signals from linear mix-

tures of those signals. ICA draws upon higher-order

signal statistics to determine a set of ”components”

which are maximally independent of each other.

The aims of this paper is to investigate which ICA

algorithm is best adapted to deal as a preprocessing

stage with EEG signals. In order to do that, we made

different experiments with EEG data from Alzheimer

and age-match control subjects. The evaluation was

calculated in terms of measure of receiver operating

characteristic (ROC) score.

The paper is organized as follows: in Section 2

we present experimental data characteristics used in

the experiments. Section 3 is devoted to procedure

and ICA algorithms used. In Section 4 we explain

the measure that we will use for obtaining the experi-

mental results, that are presented in Section 5. Finally,

conclusions are presented in Section 6.

2 EXPERIMENTAL DATA

In the course of a clinical study, mutlichannel EEG

recordings (Deltamed EEG machine) were recorded

from 33 elderly patients affected by Alzheimer’s dis-

ease and followed clinically (labeled AD set) and

from 39 age-matched controls (labeled Control set),

with electrodes located on 19 sites according to

the 10-20 international system. This database was

recorded in normal routine. Reference electrodes

were placed between Fz and Cz, and between Cz and

Pz. The sampling frequency was 256 Hz, with band-

pass ﬁlter 0.17-100 Hz. Three periods of 5 seconds

were selected in a ”rest eyes-closed” condition for

each patients. In selecting these three independent

sessions, an artifact rejection procedure was used to

help minimize the artifact effect.

3 ICA AND BSS

3.1 Procedure

At the ﬁrst stage, we apply principal component anal-

ysis (PCA) to perform dimensionality reduction. At

the second stage, an ICA algorithm is implemented to

perform BSS. The estimated output signal y

are as-

sumed to be the source signals of interest up certain

scaling and permutation ambiguity.

In addition, if we are only interested in denoising

or getting rid of speciﬁc component, we can set that

speciﬁc output signal (say y

) to zero while keeping

other components intact, and apply back projection

procedure to recover the original scene. In our ex-

periments, in ranking the output components, we al-

232

Solé-Casals J., Vialatte F., Chen Z. and Cichocki A. (2008).

INVESTIGATION OF ICA ALGORITHMS FOR FEATURE EXTRACTION OF EEG SIGNALS IN DISCRIMINATION OF ALZHEIMER DISEASE.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 232-235

DOI: 10.5220/0001061902320235

 SciTePress

ways select the one that has the least absolute kurtosis

value (i.e., the one close to Gaussian by assuming zero

kurtosis statistic for Gaussian signal, positive kurtosis

statistic for super-Gaussian signal, and negative kur-

tosis for sub-Gaussian signal).

3.2 Selection of Candidate Algorithms

For comparison, we have selected seven representa-

tive ICA algorithms. The selection criteria of them

are based on several factors: (i) computationally efﬁ-

ciency; (ii) robustness; (iii) fewer degree of freedom

(such as the choices of learning rate parameter, non-

linearity, or number of iterations); (iv) preference to

batch method.

Speciﬁcally, the following seven ICA/BSS algo-

rithms are among some of most popular BSS meth-

ods in the literature: AMUSE, SOBI, JADE, Pearson-

ICA, Thin-ICA, CCA-BSS and TFD-BSS.

The detailed description of algorithms are ne-

glected here; for relevant references, see (Cichocki

and Amari, 2002). All of algorithms are implemented

in MATLAB, some of them are available for down-

load from the original contributors (Cichocki et al., ).

For each algorithm, we have varied the number of

independent components (namely, n), from 3 to 10, to

extract the resultant uncorrelatedor independent com-

ponents.

4 PERFORMANCE EVALUATION

In signal detection/classiﬁcation theory, a receiver op-

erating characteristic (ROC) is a graphical plot of the

sensitivity vs (1-speciﬁcity) for a binary classiﬁer sys-

tem as its discrimination threshold is varied. The

ROC can also be represented equivalently by plotting

the fraction of true positives (TP) vs the fraction of

true negatives (TN). Nowadays, the usage of ROC has

become a common measure to evaluate the discrimi-

nation ability of the feature or classiﬁer. Roughly, the

discrimination ability or performance is measured by

the area value underneath the ROC curve, the greater

the value, the better is the performance (with 1 denot-

ing perfect classiﬁcation, and 0.5 denoting pure ran-

dom guess).

Since the primary purpose here is to evaluate the

features extracted from different ICA algorithms, we

have focused on the comparison between ICA algo-

rithms and the choice of number of independent com-

ponents. In order to obtain the baseline, we choose

two simple yet popular linear classiﬁers—the linear

discriminant analysis (LDA) and linear perceptron.

In calculating the ROC score, we have employed the

leave-one-out (LOO) procedure.

The features we use to feed the linear classiﬁer are

the power values extracted from different frequency

bands (θ, α, β, and γ). The ROC score is ﬁrst calcu-

lated by using raw EEG data without any ICA prepro-

cessing; this ROC score is regarded as a baseline for

further comparison. For ICA feature extraction, we

conduct the procedures of dimensionality reduction,

source separation, component rejection, followed by

backward projection. For each algorithm, we calcu-

late their ROC score by varying the number of in-

dependent components from 3 to 10. Note that all

the discrimination tasks are binary classiﬁcation: AD

against control subjects.

5 EXPERIMENTAL RESULTS

First, we calculated the ROC score for all ICA al-

gorithm with varying number of independent compo-

nents. All algorithms follow the similar-shape trend:

compared to baseline, there is a positive gain in high-

frequency bands using ICA; while for low-frequency

bands, there is no need for using ICA because of their

negative gains. In fact, the result is consistent with

what was expected: since the SNR is poor in high-

frequencybands, eliminating the independent compo-

nent with the least absolute value of kurtosis would

lead to a gain in SNR; consequently, the ROC score

or its gain is greater.

Next, the comparison was conducted on three in-

dividual 5-second sessions’ EEG recordings. By av-

eraging these three independent data set, we also ob-

tain the performance comparison. It can be seen from

these results that for all independent data sets, the

performance depends on the choice of the ICA algo-

rithm as well as the choice of components. On the

other hand, it is also obvious that by using ICA algo-

rithms for feature extraction, it is possible to boost the

ROC score performance (w.r.t. the baseline) around

0.7467−0.6193

0.6193

= 20.6% (data set 1), 15.6% (data set 2),

and 10.2% (data set 3), assuming the best ICA al-

gorithm (with optimum number of IC) is employed.

This improvement is quite signiﬁcant. The averaged

ROC score against the number of independent com-

ponents is plotted in Figure 1.

From Table 1, several noteworthy observations

are in order:

• It seems that the optimum number of IC is 4, ob-

taining the highest mean ROC score (averaged

over all ICA algorithms) 0.6536, followed by

0.6447 (IC=6). Overall, it seems the optimal

range for the number of IC is between 4 to 7.

INVESTIGATION OF ICA ALGORITHMS FOR FEATURE EXTRACTION OF EEG SIGNALS IN DISCRIMINATION

OF ALZHEIMER DISEASE

233

theta alpha1 alpha2 beta1 beta2 gamma

−0.06

−0.05

−0.04

−0.03

−0.02

−0.01

0.01

0.02

Mean ROC score gain

AMUSE

SOBI

JADE

Pearson−ICA

thin−ICA

CCA−BSS

TFD−BSS

3 4 5 6 7 8 9 10

0.52

0.54

0.56

0.58

0.6

0.62

0.64

0.66

0.68

0.7

Number of independent components

Averaged ROC score

AMUSE

SOBI

JADE

Pearson−ICA

thin−ICA

CCA−BSS

TFD−BSS

Figure 1: Left: The mean ROC score gain (averaged over 3 data bases and the number of independent components; with 0 as

baseline) against frequency bands for 7 algorithms. Rigth: The averaged ROC score (over 3 data bases) comparison between

different ICA algorithms with varying number of independent components.

• By averaging different numbers of IC, it seems

the overall best ICA algorithms are Pearson-ICA

and JADE (averaged from 4 to 7 components), or

Thin-ICA and SOBI (averaged from 3 to 10 com-

ponents).

• Overall, JADE and SOBI seem to give quite con-

sistent performance for different number of com-

ponents.

In addition, we can also compare the correct clas-

siﬁcation rate between different ICA algorithms with

different setups. The results using LDA and linear

perceptron classiﬁers are summarized in Table 2.

Likewise, compared to the baseline correct classiﬁ-

cation rate, the performance with appropriate ICA al-

gorithm give some more or less improvement.

6 CONCLUSIONS

In this work, we have proposed a measure or crite-

ria to compare several popular ICA algorithms in the

investigation of feature extraction of eeg signals in

discrimination of Alzheimer disease. As a powerful

signal processing tool used in the preprocessing step,

ICA was found useful in artifact rejection, improving

SNR, and noise reduction, all of which are important

for the feature selection at the later stage. The ICA

algorithms and the optimum choice of independent

components are extensively investigated using sim-

ple linear classiﬁers and LOO procedure for calculat-

ing the resultant ROC scores and correct classiﬁcation

rate, both compared to their baselines.

It was found, in general, ICA algorithms are par-

ticularly useful for feature extraction in high fre-

quency bands, especially on high alpha and beta

ranges; where in low frequency bands, little gain has

been obtained comparedto the baselines. This is more

or less anticipated, because EEG signals are usually

contaminated by noise at high frequency bands, but

are more resistant to noise at low frequency bands.

Moreover, the optimum number of selected compo-

nents seem to depend on the selected algorithms,

but overall observations seem to indicate the number

should be in the range from 4 to 7. Interestingly, this

number is consistent with our early independent in-

vestigations (Vialatte and et al., 2005). In terms of

overall average performance, it seem that the JADE,

SOBI, thinICA, and CCABSS algorithms give more

consistent and better results.

ACKNOWLEDGEMENTS

This work has been partly funded by the Departament

d’Universitats, Recerca i Societat de la Informaci´o

de la Generalitat de Catalunya and by the Ministe-

rio de Educaci´on y Ciencia under the grant TEC2007-

61535/TCM

REFERENCES

Cichocki, A. and Amari, S. (2002). Adaptive Blind Signal

and Image Processing. Wiley, New York.

Cichocki, A., Amari, S., and et al. Icalab toolboxes.

http://www.bsp.brain.riken.jp/ICALAB.

Vialatte, F. and et al. (2005). Blind source separation and

sparse bump modelling of time frequency representa-

tion of eeg signals: New tools for early detection of

alzheimer’s disease. In Proc. IEEE Work. Machine

Learning for Signal Processing, pp. 27–32.

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

234

Table 1: The ROC score comparison between ICA algorithms by averaging the results from three 5-second sessions. The

baseline value (without ICA) of ROC score is 0.63. The bold fonts indicate the top two winners or the maximal two values in

each column.

no. IC AMUSE SOBI JADE Pearson-ICA Thin-ICA CCA-BSS TFBSS

3 0.5791 0.6358 0.5379 0.5618 0.5569 0.6255 0.5755

4 0.6496 0.6566 0.6729 0.6729 0.6506 0.6496 0.6234

5 0.6265 0.6154 0.6099 0.5983 0.6408 0.6182 0.6343

6 0.6299 0.6369 0.6654 0.6649 0.6457 0.6511 0.6193

7 0.5998 0.6346 0.6325 0.6485 0.6496 0.6097 0.6382

8 0.6263 0.6270 0.6317 0.6203 0.6475 0.6141 0.6213

9 0.6203 0.6094 0.6382 0.6123 0.6289 0.6250 0.6402

10 0.6327 0.6244 0.6265 0.6055 0.6348 0.6131 0.6244

average from {2, 3, 4, 5} rows

0.6292 0.6210 0.6498 0.6544 0.6385 0.6094 0.6364

average from all rows

0.6205 0.6300 0.6269 0.6231 0.6318 0.6258 0.6221

Table 2: Classiﬁcation results using leave-one-out procedure. The bold font indicate the maximum value in each column.

no. IC AMUSE SOBI JADE Pearson-ICA Thin-ICA CCA-BSS TFBSS

LDA baseline value (without ICA) 75%

3 65.2778 65.2778 65.2778 63.8889 62.5000 61.1111 62.5000

4 69.4444 73.6111 70.8333 69.4444 68.0556 69.4444 61.1111

5 68.0556 66.6667 77.7778 69.4444 75.0000 73.6111 69.4444

6 72.2222 72.2222 76.3889 75.0000 70.8333 77.7778 69.4444

7 68.0556 73.6111 70.8333 76.3889 72.2222 72.2222 72.2222

8 73.6111 76.3889 72.2222 73.6111 72.2222 70.8333 70.8333

9 76.3889 73.6111 68.0556 73.6111 76.3889 69.4444 70.8333

10 70.8333 75.0000 73.6111 72.2222 77.7778 72.2222 76.3889

linear perceptron baseline value (without ICA) 62.5%

3 59.7222 51.3889 54.1667 54.1667 45.8333 68.0556 54.1667

4 62.5000 70.8333 65.2778 70.8333 68.0556 56.9444 54.1667

5 59.7222 62.5000 56.9444 56.9444 68.0556 62.5000 62.5000

6 65.2778 62.5000 70.8333 65.2778 68.0556 65.2778 62.5000

7 59.7222 56.9444 65.2778 65.2778 62.5000 65.2778 65.2778

8 65.2778 62.5000 59.7222 59.7222 65.2778 65.2778 62.5000

9 62.5000 62.5000 70.8333 56.9444 68.0556 62.5000 62.5000

10 62.5000 62.5000 68.0556 59.7222 62.5000 65.2778 59.7222

INVESTIGATION OF ICA ALGORITHMS FOR FEATURE EXTRACTION OF EEG SIGNALS IN DISCRIMINATION

OF ALZHEIMER DISEASE

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